Despite significant federal and state investments to transition patient medical records to all-electronic systems, and a generalized expectation of policy makers that quality of healthcare could someday be precisely defined and measured, a definitive correlation between healthcare quality and cost has remained out of reach. Petersen et al. in an extensive review of the literature compared various methods to improve quality through pay-for-performance programs. (Petersen L, Woodard L, Urech T, et al.: Does pay-for-performance improve the quality of health care? Ann Intern Med 145:265-272 2006). Their analysis concluded that most financial incentives were focused on the delivery of prevention services rather than health outcomes. Other investigators reported that so-called pay-for-performance programs impact some patients negatively, particularly those with mental illness and chemical dependency. (Shen Y: Selection incentives in a performance-based contracting system. Health Serv. Res. 38:535-552 2003; Norton E: Incentive regulation of nursing homes. J. Health Econ. 11:105-128 1992; Rosenthal M, Frank R, Li Z, et al: Early experience with pay-for-performance: from concept to practice. JAMA. 294:1788-1793 2005). Such analyses reveal that American healthcare competes on delivery of the lowest procedure price rather than a value-based outcome for individual patients. (Porter M, Teisberg E: Redefining Healthcare. Harvard Business Press, ISBN 1-59139-778-2, 2006; Baker L: Measuring competition in health care markets. Health Serv. Res. 36: 223-251, 2001; Scanlon D, Swaminathan S, Lee W, et al.: Does competition improve health care quality? Health Serv. Res. 43: 1931-1951 2008.)
In order to measure treatment outcomes and compensate providers fairly, improved measuring tools are necessary. Currently, most payers score quality care based on delivery of services focused in prevention such as up-to-date immunizations, early diagnostic studies such as mammography, colonoscopy, PAP smears, PSA testing, or education in healthy life styles. (Landon B, Zaslaysky A, Beaulieu J, et al.: Health plan characteristics and consumer's assessments of quality. Health Affairs 20: 274-286, 2001; Scanlon D, Darby C, Rolph E, et al.: The role of performance measures for improving quality in managed care organizations. Health Sery Res. 36: 619-641, 2001.) Though these services are valuable, patients still develop chronic illnesses that require treatment or palliative care. Indeed, treatment for such chronic conditions represents a large portion of healthcare budgets. In order to grade treatment outcomes fairly, each patient should be scored as to their level of illness complexity prior to the start of treatment, so that outcomes are judged among patients of similar severity.
Currently, disease “staging” is a prime method for relating disease severity to reimbursement levels. Chronic kidney disease (“CKD”) typifies such a condition with five stages of severity based on a declining glomerular filtration rate. However, many of these patients are at risk for higher complexity due to co-morbid factors like hypertension, diabetes, and congestive heart failure. CKD is often associated with multiple organ dysfunctions that impact cost, health, and work productivity, the diversity of treatment modalities required to care for these patients may lead to disagreements between providers and payers on therapy approval and reimbursement. Unfortunately, payers may have incomplete information about the severity of these co-existing morbidities, and therefore must rely primarily on a general CKD staging to evaluate quality care. Payment by stage of illness also provides a convenient method to aggregate cost and grade treatment upon the overall public health. (Johnson C, Levey A, Coresh J, el al.: Clinical practice guidelines for chronic kidney disease in adults: Part 1. Definition, disease stages, evaluation, treatment, and risk factors. American Family Physician 70: 869-876, 2004; Smith D, Gullion C, Nichols G, et al.: Cost of medical care for chronic kidney disease and comorbidity among enrollees in a large HMO population. J. Amer Soc Nephrology 15: 1300-1306, 2004.)
Unfortunately, clinical experience suggests that these ordinal measures for renal disease, along with other diseases, though ideal for population reports, do not fully account for illness complexity seen in individual patients. When pay-for-performance is linked to grading of illness by stage, it may imply quality on a population basis, however, if the true level of illness complexity at the start of treatment is unknown, then the value of any outcome compared to the cost in achieving it, also remains unknown. (Born P, Simon C.: Patients and profits: the relationship between HMO financial performance and quality of care. Health Affairs 20: 167-174, 2001; Kessler D, Geppert J.: The effects of competition on variation in the quality and cost of medical care. Jour of Economics and Management Strategy 14: 575-589, 2005; McGlynn E, Asch S, Adams J, et al.: The quality of health care delivered to adults in the United States. New England Journal of Medicine 348: 2635-2645, 2003.)
With the introduction of Accountable Care Organizations (“ACO”) in the United States, there is a new focus on provider compensation. Under this system, providers are encouraged to enter into risk adjusted capitation agreements within a patient-centered medical home. Under this system, determining risk on small patient groups could prove difficult and compel both payers and providers to accept reimbursement based on population averages not reflecting unique features within given population groups, such as population groups of different ethnicity or of different locality.
Healthcare providers and consumers are both investigating ways to reduce costs in providing healthcare services and treatment, while still maintaining or improving patient outcomes. Some metrics are used to track the performance of healthcare providers, but most quality assurance systems use claims to infer population outcomes. Numerous quality metrics currently exist but nearly all are based on claims data analysis, which relates the number and cost for specific treatment procedures (CPT Codes) to individual diagnostic codes (ICD-9, ICD-10) for patient illness. All of the currently existing quality metrics use quality measures based on tabulation of preventative measures deployed within a population, which fail to provide a patient and provider specific analysis.